code: def __init__(
      self, prediction_len: int, num_parallel_samples: int, **kwargs
  ) -> None:
      super().__init__(**kwargs)
      self.prediction_len = prediction_len
      self.num_parallel_samples = num_parallel_samples
  
file_path: gluonts/src/gluonts/mx/model/canonical/_network.py
imports: from mxnet.gluon import HybridBlock
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.block.feature import FeatureEmbedder
  from gluonts.mx.block.scaler import MeanScaler
  from gluonts.mx.distribution import DistributionOutput
label_desc: Use image node_id 1 to create a new CanonicalPredictionNetwork object from inherited base classes: CanonicalNetworkBase with example: obj = CanonicalPredictionNetwork(prediction_len, num_parallel_samples)